Leveraging Large Language Models and Self-Training for Effective Cross-Domain Sentence Pattern Structure Parsing

Core Concepts

An innovative LLM-enhanced self-training approach that leverages partial syntactic rules and target domain sentences to dynamically generate training data, enabling effective cross-domain adaptation of Sentence Pattern Structure (SPS) parsing.

Abstract

The paper presents a novel task of cross-domain SPS parsing and proposes an innovative LLM-enhanced self-training method to address this challenge. The key highlights are:

Existing SPS parsers heavily rely on textbook corpora for training, lacking cross-domain capability. The authors explore the utilization of large language models (LLMs) to enhance the adaptability and flexibility of SPS parsing.

The proposed method dynamically embeds LLMs into the iterative self-training process. Partial syntactic rules from the source domain are combined with target domain sentences to generate specialized training data for the target domain.

To address the instability and hallucination issues of LLMs, the authors incorporate rule-based methods in both data generation and pseudotree selection, leveraging the structural advantages of the data while minimizing the adverse effects caused by a lack of flexibility and potential errors.

Experiments on textbook and news domains demonstrate the effectiveness of the proposed method, outperforming rule-based baselines by 1.68 points on F1 metrics. The results validate the ability of the LLM-enhanced self-training approach to facilitate cross-domain adaptation of SPS parsing.

Stats

"各项指标增幅远远高于发展速度(The growth rates of all indicators far exceed the development speed)".
"中美合作高科技项目签字仪式"今天在上海举行。(The "China-US High-Tech Project Signing Ceremony" was held in Shanghai today.)

Quotes

"Sentence Pattern Structure (SPS) parsing is a syntactic analysis method primarily employed in language teaching."
"Existing SPS parsers rely heavily on textbook corpora for training, lacking cross-domain capability."
"To overcome this constraint, this paper proposes an innovative approach leveraging large language models (LLMs) within a self-training framework."

Key Insights Distilled From

Cross-domain Chinese Sentence Pattern Parsing

by Jingsi Yu,Cu... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2402.16311.pdf

Deeper Inquiries

How can the proposed LLM-enhanced self-training approach be extended to other grammar formalisms beyond SPS parsing, such as constituency or dependency parsing?

The LLM-enhanced self-training approach proposed in the context can be extended to other grammar formalisms like constituency or dependency parsing by adapting the methodology to suit the specific requirements of these tasks. For constituency parsing, the LLMs can be utilized to generate training data that adheres to constituency tree structures, similar to how they were used to generate sentences following SPS rules. The LLMs can be prompted with syntactic rules and example sentences specific to constituency parsing to generate diverse training data for this task. Similarly, for dependency parsing, the LLMs can be leveraged to generate sentences that capture the modifying relationships between words, which are crucial in dependency parsing. By providing the LLMs with appropriate prompts and constraints, the generated data can be tailored to suit the requirements of dependency parsing tasks.

What are the potential limitations of relying on rule-based methods for instance selection, and how can the authors further improve the robustness of their approach?

Relying solely on rule-based methods for instance selection may have limitations in terms of adaptability to diverse domains and potential errors in rule application. Rule-based methods may not capture all the nuances and variations present in the data, leading to suboptimal instance selection. To improve the robustness of their approach, the authors can consider the following enhancements:

Hybrid Approach: Combining rule-based methods with data-driven approaches can provide a more comprehensive selection process. Utilizing machine learning models to complement rule-based selection can enhance the adaptability and accuracy of instance selection.
Dynamic Rule Updating: Regularly updating and refining the rules based on feedback from the model's performance can help in addressing limitations and improving the rule-based selection process.
Ensemble Methods: Employing ensemble methods that combine multiple instance selection criteria, including rule-based and data-driven approaches, can enhance the robustness of the selection process by leveraging the strengths of each method.
Human Validation: Incorporating human validation or feedback loops to verify the selected instances can help in identifying and correcting any errors or biases introduced by the rule-based selection process.

Given the success of the LLM-enhanced method in cross-domain adaptation, how can the insights from this work be applied to enhance the performance of SPS parsing in low-resource settings or multilingual scenarios?

The insights from the successful application of the LLM-enhanced method in cross-domain adaptation can be leveraged to enhance the performance of SPS parsing in low-resource settings or multilingual scenarios by:

Data Augmentation: Using LLMs to generate synthetic data in low-resource settings can help in expanding the training data and improving the robustness of the SPS parser.
Transfer Learning: Pre-training LLMs on multilingual data and then fine-tuning them on specific low-resource languages can enable the models to capture language-specific syntactic patterns, which can benefit SPS parsing in multilingual scenarios.
Domain Adaptation: Applying the self-training framework with LLMs in low-resource or multilingual settings can facilitate domain adaptation by dynamically generating training data specific to the target domain or language.
Error Analysis: Conducting thorough error analysis using LLMs can help in identifying common syntactic errors or challenges in low-resource or multilingual settings, enabling targeted improvements in the SPS parsing model.